Deep learning-based ROI detection of AEH and EC on histopathology WSIs for predicting hormonal treatment response

Endometrial cancer (EC) is the most common gynecologic malignancy in the United States. Hormone therapies and hysterectomy are viable treatments for early-stage EC and atypical endometrial hyperplasia (AEH), a high-risk precursor to EC. Prediction of patient response to hormonal treatment is useful for patients to make treatment decisions. We have previously developed a mix-supervised model: a weakly supervised deep learning model for hormonal treatment response prediction based on pathologist-annotated AEH and EC regions on whole slide images of H&E stained slides. The reliance on pathologist annotation in applying the model to new cases is cumbersome and subject to inter-observer variability. In this study, we automate the task of ROI detection by developing a supervised deep learning model to detect AEH and EC regions. This model achieved a patch-wise AUROC performance of 0.974 (approximate 95% CI [0.972, 0.976]). The mixsupervised model yielded a patient-level AUROC of 0.76 (95% CI [0.59, 0.92]) with ROIs detected by our new model on a hold-out test set in the task of classifying patients into responders and non-responders. As a comparison, the original model as tested on pathologist-annotated ROIs achieved an AUROC of 0.80 with 95% CI [0.63, 0.95]. Our results demonstrate the potential of using weakly supervised deep learning and supervised ROI detection model for predicting hormonal treatment response in endometrial cancer patients.